Many enterprises, such as companies, corporations, and organizations, are creating and deploying “enterprise data warehouses” to serve as the single source of corporate data for business intelligence. As referred herein, a data warehouse is a data storage or repository that includes one or more electrical or electronic data storage devices such as computers, servers, computer databases, and the like. Also, as referred herein and understood in the art, business intelligence (BI) includes applications and technologies that work together to collect, provide access to, and analyze data and information about operations of a business or a desired entity. Enterprise data warehouses are expected to both scale to enormous data volumes (hundreds of terabytes) and perform well under increasingly complex workloads, which typically include batch and incremental data loads, batch reports, and/or complex ad hoc queries. A key challenge is to manage each complex workload in order to meet stringent performance objectives. For example, batch load tasks may be required to finish within a specified time window before reports or queries can be serviced, batch reports may issue thousands of “roll-up” (aggregation) queries that are required to complete within a specified time window, and ad hoc queries may have user-specified deadlines and priorities.
Accordingly, there is a desire to have effective workload management of an enterprise data warehouse (EDW) in order to allocate resources therein and execute workloads assigned thereto so as to meet desired performance objectives.